{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有特征串联起来，构成RS_Train.csv\n",
    "#RS_Test.csv\n",
    "#为最后推荐系统做准备\n",
    "from __future__ import division\n",
    "\n",
    "import cPickle\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "from numpy.random import random  \n",
    "from collections import defaultdict\n",
    "\n",
    "#相似度/距离\n",
    "import scipy.spatial.distance as ssd\n",
    "\n",
    "class RecommonderSystem:\n",
    "    def __init__(self):\n",
    "        # 读入数据做初始化\n",
    "\n",
    "        #用户和活动新的索引\n",
    "        self.userIndex = cPickle.load(open(\"PE_userIndex.pkl\", 'rb'))\n",
    "        self.eventIndex = cPickle.load(open(\"PE_eventIndex.pkl\", 'rb'))\n",
    "        self.n_users = len(self.userIndex)\n",
    "        self.n_items = len(self.eventIndex)\n",
    "\n",
    "        #用户-活动关系矩阵R\n",
    "        #在train_SVD会重新从文件中读取,二者要求的格式不同，来不及统一了:(\n",
    "        self.userEventScores = sio.mmread(\"PE_userEventScores\").todense()\n",
    "\n",
    "        #倒排表\n",
    "        ##每个用户参加的事件\n",
    "        self.itemsForUser = cPickle.load(open(\"PE_eventsForUser.pkl\", 'rb'))\n",
    "        ##事件参加的用户\n",
    "        self.usersForItem = cPickle.load(open(\"PE_usersForEvent.pkl\", 'rb'))\n",
    "\n",
    "        #基于模型的协同过滤参数初始化,训练\n",
    "        self.init_SVD()\n",
    "        self.train_SVD(trainfile = \"train.csv\")\n",
    "\n",
    "        #根据用户属性计算出的用户之间的相似度\n",
    "        self.userSimMatrix = sio.mmread(\"US_userSimMatrix\").todense()\n",
    "\n",
    "        #根据活动属性计算出的活动之间的相似度\n",
    "        self.eventPropSim = sio.mmread(\"EV_eventPropSim\").todense()\n",
    "        self.eventContSim = sio.mmread(\"EV_eventContSim\").todense()\n",
    "\n",
    "        #每个用户的朋友的数目\n",
    "        self.numFriends = sio.mmread(\"UF_numFriends\")\n",
    "        #用户的每个朋友参加活动的分数对该用户的影响\n",
    "        self.userFriends = sio.mmread(\"UF_userFriends\").todense()\n",
    "\n",
    "        #活动本身的热度\n",
    "        self.eventPopularity = sio.mmread(\"EA_eventPopularity\").todense()\n",
    "\n",
    "    def init_SVD(self, K=20):\n",
    "        #初始化模型参数（for 基于模型的协同过滤SVD_CF）\n",
    "        self.K = K  \n",
    "\n",
    "        #init parameters\n",
    "        #bias\n",
    "        self.bi = np.zeros(self.n_items)  \n",
    "        self.bu = np.zeros(self.n_users)  \n",
    "\n",
    "        #the small matrix\n",
    "        self.P = random((self.n_users,self.K))/10*(np.sqrt(self.K))\n",
    "        self.Q = random((self.K, self.n_items))/10*(np.sqrt(self.K))  \n",
    "                  \n",
    "          \n",
    "    def train_SVD(self,trainfile = 'train.csv', steps=100,gamma=0.04,Lambda=0.15):\n",
    "        #训练SVD模型（for 基于模型的协同过滤SVD_CF）\n",
    "        #gamma：为学习率\n",
    "        #Lambda：正则参数\n",
    "\n",
    "        #偷懒了，为了和原来的代码的输入接口一样，直接从训练文件中去读取数据\n",
    "        print \"SVD Train...\"\n",
    "        ftrain = open(trainfile, 'r')\n",
    "        ftrain.readline()\n",
    "        self.mu = 0.0\n",
    "        n_records = 0\n",
    "        uids = []  #每条记录的用户索引\n",
    "        i_ids = [] #每条记录的item索引\n",
    "        #用户-Item关系矩阵R（内容同userEventScores相同），临时变量，训练完了R不再需要\n",
    "        R = np.zeros((self.n_users, self.n_items))\n",
    "\n",
    "        for line in ftrain:\n",
    "            cols = line.strip().split(\",\")\n",
    "            u = self.userIndex[cols[0]]  #用户\n",
    "            i = self.eventIndex[cols[1]] #活动\n",
    "\n",
    "            uids.append(u)\n",
    "            i_ids.append(i)\n",
    "\n",
    "            R[u,i] = int(cols[4])  #interested\n",
    "            self.mu += R[u,i]\n",
    "            n_records += 1\n",
    "\n",
    "        ftrain.close()\n",
    "        self.mu /= n_records\n",
    "\n",
    "        # 请补充完整SVD模型训练过程   \n",
    "\n",
    "        for step in range(steps):\n",
    "            rmse_sum=0.0\n",
    "\n",
    "            #将训练样本打散顺序\n",
    "            kk = np.random.permutation(n_records)\n",
    "            for j in range(n_records):\n",
    "                #每次一个训练样本\n",
    "                index = kk[j]\n",
    "                u = uids[index]\n",
    "                i = i_ids[index]\n",
    "\n",
    "                #预测残差\n",
    "                eui = R[u,i] - self.pred_SVD(u,i)\n",
    "\n",
    "                #残差平分和\n",
    "                rmse_sum+=eui**2\n",
    "\n",
    "                #随机梯度下降，更新\n",
    "                self.bu[u] += gamma*(eui - Lambda*self.bu[u])\n",
    "                self.bi[i] += gamma*(eui - Lambda*self.bi[i])\n",
    "\n",
    "                for k in range(self.K):\n",
    "                    self.P[u,k] += gamma * eui * self.Q[k,i] - Lambda * self.P[u,k]\n",
    "                    self.Q[k,i] += gamma * eui * self.P[u,k] - Lambda * self.Q[k,i]\n",
    "\n",
    "            # 学习率递减\n",
    "            gamma = gamma*0.93\n",
    "        print \"SVD trained\"\n",
    "\n",
    "    def pred_SVD(self, uid, i_id):\n",
    "        #根据当前参数，预测用户uid对Item（i_id）的打分        \n",
    "        ans=self.mu + self.bi[i_id] + self.bu[uid] + np.dot(self.P[uid,:],self.Q[:,i_id])  \n",
    "\n",
    "        #将打分范围控制在0-1之间\n",
    "        if ans>1:  \n",
    "            return 1  \n",
    "        elif ans<0:  \n",
    "            return 0\n",
    "        return ans  \n",
    "\n",
    "    def sim_cal_UserCF(self, uid1, uid2 ):\n",
    "        #请补充基于用户的协同过滤中的两个用户uid1和uid2之间的相似度（根据两个用户对item打分的相似度）\n",
    "        similarity = 0.\n",
    "        \n",
    "        si={} # 有效item，即两个用户准有打分的item的集合，之前\n",
    "        for item in self.itemsForUser[uid1]:# uid1所有打过分的item\n",
    "            if item in self.itemsForUser[uid2]: # 如果uid2也对item打过分\n",
    "                si[item]=1 # item为一个有效item\n",
    "                \n",
    "        n=len(si)\n",
    "        if(n==0):# 没有共同打过分的item，相似度设置为0\n",
    "            similarity = 0\n",
    "        else:\n",
    "            #用户uid1打过分的所有有效的item\n",
    "            items1=np.array([self.userEventScores[uid1,item] for item in si])\n",
    "            \n",
    "            #用户uid2打过分的所有有效的item\n",
    "            items2=np.array([self.userEventScores[uid2,item] for item in si])\n",
    "\n",
    "            # Todo: 这里直接调用了包方法，最好需要自己实现一下Pearson公式\n",
    "            similarity = ssd.correlation(items1,items2)\n",
    "\n",
    "        return similarity  \n",
    "\n",
    "    def userCFReco(self, userId, eventId):\n",
    "        \"\"\"\n",
    "        根据User-based协同过滤，得到event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i\n",
    "          for every other user v that has a preference for i\n",
    "            compute similarity s between u and v\n",
    "            incorporate v's preference for i weighted by s into running aversge\n",
    "        return top items ranked by weighted average\n",
    "        \"\"\"\n",
    "        #请补充完整代码\n",
    "        ans = 0.0       \n",
    "\n",
    "        \"\"\"\n",
    "        之前算法，将当前userId所打过分的item，作为给当前eventId打过分用户的，计算相似度的特征向量\n",
    "        不合适，还是按共同打过分的item作为计算相似度的特征向量\n",
    "        \n",
    "        i = self.userIndex[userId]  #用户\n",
    "        j = self.eventIndex[eventId] #活动\n",
    "        \n",
    "        # 与活动j有关的用户\n",
    "        rUser = self.usersForEvent[j]\n",
    "        if i in rUser: # 从train中获取的数据，应该没有\n",
    "            rUser.remove(i)\n",
    "        n_rUser = len(rUser)\n",
    "\n",
    "        # 与用户i有关的活动\n",
    "        rEvent = self.eventsForUser[i]    \n",
    "        # 去掉待评价的活动\n",
    "        if j in rEvent: # 从train中获取的数据，应该没有\n",
    "            rEvent.remove(j)\n",
    "        n_rEvent = len(rEvent)\n",
    "\n",
    "        # 当前用户对活动的评价矩阵\n",
    "        CR = ss.dok_matrix((1, n_rEvent))\n",
    "        for j2, jj in enumerate(rEvent):        \n",
    "            if self.userEventScores[i,jj] == 0:\n",
    "                CR[0,j2] = -0.1 # 针对未评价的\n",
    "            else:\n",
    "                CR[0,j2] = self.userEventScores[i,jj]\n",
    "\n",
    "        # 临时变量，存放于i,j相关的用户-活动矩阵，用于计算用户相似性\n",
    "        R = ss.dok_matrix((n_rUser, n_rEvent))\n",
    "\n",
    "        sumOfsim = 0 # 相似性之和\n",
    "        sumOfProOfevalAndsim = 0 #各用户对j活动评价*相似性之和\n",
    "\n",
    "        # 将userEventScores相关的评价值，存入R中\n",
    "        for i2,ii in enumerate(rUser):\n",
    "            for j2, jj in enumerate(rEvent):\n",
    "                if self.userEventScores[ii,jj] == 0:\n",
    "                    R[i2,j2] = -0.1 # 针对未评价的\n",
    "                else:\n",
    "                    R[i2,j2] = self.userEventScores[ii,jj]\n",
    "\n",
    "            # 计算用户相似性\n",
    "            usim = 0.0\n",
    "            usim = sim_cal_UserCF(self,CR.getrow(0).todense(),R.getrow(i2).todense())\n",
    "            if usim >= 0.6:\n",
    "                # 各用户对j活动评价*相似性之和\n",
    "                sumOfProOfevalAndsim += self.userEventScores[ii,j] * usim\n",
    "                # 相似性之和\n",
    "                sumOfsim += usim\n",
    "\n",
    "        # 计算i用户对j活动的评价\n",
    "        aveEval = CR.sum(axis=1)/n_rEvent\n",
    "\n",
    "        # i用户对j活动的预测评价    \n",
    "        if sumOfsim > 0:\n",
    "            ans = aveEval + sumOfProOfevalAndsim/sumOfsim\n",
    "        \"\"\"\n",
    "        u = self.userIndex[userId]  #用户\n",
    "        i = self.eventIndex[eventId] #活动\n",
    "        \n",
    "        sim_accumulate=0.0\n",
    "        rat_accumulate=0.0\n",
    "        \n",
    "        for user in self.usersForItem[i]: #对eventId打过分的所有用户\n",
    "            sim = self.sim_cal_UserCF(uid1 = user,uid2 = u)\n",
    "            if sim == 0:continue\n",
    "                \n",
    "            rat_accumulate += sim * self.userEventScores[user,i]\n",
    "            sim_accumulate += sim\n",
    "        \n",
    "        if sim_accumulate==0:# 没有一个用户与userId相似的，无法预测打分\n",
    "            return self.mu\n",
    "        ans = rat_accumulate/sim_accumulate\n",
    "        \n",
    "        # 将打分范围控制在0-1之间\n",
    "        if ans>1:\n",
    "            return 1\n",
    "        elif ans<0:\n",
    "            return 0\n",
    "        \n",
    "        return ans\n",
    "\n",
    "\n",
    "    def sim_cal_ItemCF(self, i_id1, i_id2):\n",
    "        #计算Item i_id1和i_id2之间的相似性\n",
    "        #请补充完整代码\n",
    "        similarity = 0.0\n",
    "        su={} # 有效用户集合\n",
    "        for user in self.usersForItem[i_id1]:#所有对item1打过分的user\n",
    "            if user in self.usersForItem[i_id2]:#如果该用户对item2也打过分\n",
    "                su[user]=1 #user为一个有效用户\n",
    "                \n",
    "        n=len(su)\n",
    "        if(n==0):\n",
    "            return 0\n",
    "        \n",
    "        user1=np.array([self.userEventScores[u,i_id1] for u in su])\n",
    "        \n",
    "        user2=np.array([self.userEventScores[u,i_id2] for u in su])\n",
    "        \n",
    "        # Todo: 这里直接调用了包方法，最好需要自己实现一下余弦相似性公式\n",
    "        similarity = ssd.cosine(user1,user2)\n",
    "\n",
    "        return similarity     \n",
    "        \n",
    "    #return num/den  \n",
    "    \n",
    "    def eventCFReco(self, userId, eventId):    \n",
    "        \"\"\"\n",
    "        根据基于物品的协同过滤，得到Event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i \n",
    "            for every item j tht u has a preference for\n",
    "                compute similarity s between i and j\n",
    "                add u's preference for j weighted by s to a running average\n",
    "        return top items, ranked by weighted average\n",
    "        \"\"\"\n",
    "        #请补充完整代码\n",
    "        ans = 0.0\n",
    "\n",
    "        \"\"\"\n",
    "        i = self.userIndex[userId]  #用户\n",
    "        j = self.eventIndex[eventId] #活动\n",
    "\n",
    "\n",
    "        # 与活动j有关的用户\n",
    "        rUser = self.usersForEvent[j]\n",
    "        if i in rUser: # 从train中获取的数据，应该没有\n",
    "            rUser.remove(i)\n",
    "        n_rUser = len(rUser)\n",
    "\n",
    "        # 与用户i有关的活动\n",
    "        rEvent = self.eventsForUser[i]    \n",
    "        # 去掉待评价的活动\n",
    "        if j in rEvent: # 从train中获取的数据，应该没有\n",
    "            rEvent.remove(j)\n",
    "        n_rEvent = len(rEvent)\n",
    "\n",
    "        # 当前活动各用户的评价矩阵\n",
    "        CR = ss.dok_matrix((1, n_rUser))\n",
    "        for i2, ii in enumerate(rUser):\n",
    "            if self.userEventScores[ii,j] == 0:\n",
    "                CR[0,i2] = -0.1 # 针对未评价的\n",
    "            else:\n",
    "                CR[0,i2] = self.userEventScores[ii,j]\n",
    "\n",
    "        # 临时变量，存放于i,j相关的用户-活动矩阵，用于计算用户相似性\n",
    "        R = ss.dok_matrix((n_rEvent,n_rUser))    \n",
    "\n",
    "        sumOfsim = 0 # 相似性之和\n",
    "        sumOfProOfevalAndsim = 0 #各用户对j活动评价*相似性之和\n",
    "\n",
    "        # 将userEventScores相关的评价值，存入R中\n",
    "        for j2,jj in enumerate(rEvent):        \n",
    "            for i2, ii in enumerate(rUser):\n",
    "                if userEventScores[ii,jj] == 0:\n",
    "                    R[j2,i2] = -0.1 # 针对未评价的\n",
    "                else:\n",
    "                    R[j2,i2] = self.userEventScores[ii,jj]\n",
    "\n",
    "            # 计算活动余弦相似性\n",
    "            esim = 0.0\n",
    "            esim = sim_cal_ItemCF(self,CR.getrow(0).todense(),R.getrow(j2).todense())\n",
    "            if esim >= 0.6:            \n",
    "                # 各用户对j活动评价*相似性之和\n",
    "                sumOfProOfevalAndsim += self.userEventScores[ii,j] * esim\n",
    "                # 相似性之和\n",
    "                sumOfsim += esim    \n",
    "\n",
    "        # i用户对j活动的预测评价    \n",
    "        if sumOfsim > 0:\n",
    "            ans = sumOfProOfevalAndsim/sumOfsim\n",
    "            \n",
    "        \"\"\"\n",
    "        u = self.userIndex[userId]\n",
    "        i = self.eventIndex[eventId]\n",
    "        \n",
    "        sim_accumulate=0.0\n",
    "        rat_accumulate=0.0\n",
    "        \n",
    "        for item in self.itemsForUser[u]:\n",
    "            sim = self.sim_cal_ItemCF(item,i)\n",
    "            \n",
    "            rat_accumulate += sim * self.userEventScores[u,item]\n",
    "            sim_accumulate += sim\n",
    "            \n",
    "        if sim_accumulate == 0:\n",
    "            return self.mu\n",
    "        \n",
    "        ans = rat_accumulate/sim_accumulate\n",
    "        \n",
    "        if ans>1:\n",
    "            return 1\n",
    "        elif ans<0:\n",
    "            return 0\n",
    "\n",
    "        return ans\n",
    "    \n",
    "    def svdCFReco(self, userId, eventId):\n",
    "        #基于模型的协同过滤, SVD++/LFM\n",
    "        u = self.userIndex[userId]\n",
    "        i = self.eventIndex[eventId]\n",
    "\n",
    "        return self.pred_SVD(u,i)\n",
    "\n",
    "\n",
    "    def userReco(self, userId, eventId):\n",
    "        \"\"\"\n",
    "        类似基于User-based协同过滤，只是用户之间的相似度由用户本身的属性得到，计算event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i\n",
    "          for every other user v that has a preference for i\n",
    "            compute similarity s between u and v\n",
    "            incorporate v's preference for i weighted by s into running aversge\n",
    "        return top items ranked by weighted average\n",
    "        \"\"\"\n",
    "        i = self.userIndex[userId]\n",
    "        j = self.eventIndex[eventId]\n",
    "\n",
    "        vs = self.userEventScores[:, j]\n",
    "        sims = self.userSimMatrix[i, :]\n",
    "\n",
    "        prod = sims * vs\n",
    "\n",
    "        try:\n",
    "            return prod[0, 0] - self.userEventScores[i, j]\n",
    "        except IndexError:\n",
    "            return 0\n",
    "\n",
    "    def eventReco(self, userId, eventId):\n",
    "        \"\"\"\n",
    "        类似基于Item-based协同过滤，只是item之间的相似度由item本身的属性得到，计算Event的推荐度\n",
    "        基本的伪代码思路如下：\n",
    "        for item i \n",
    "          for every item j that u has a preference for\n",
    "            compute similarity s between i and j\n",
    "            add u's preference for j weighted by s to a running average\n",
    "        return top items, ranked by weighted average\n",
    "        \"\"\"\n",
    "        i = self.userIndex[userId]\n",
    "        j = self.eventIndex[eventId]\n",
    "        js = self.userEventScores[i, :]\n",
    "        psim = self.eventPropSim[:, j]\n",
    "        csim = self.eventContSim[:, j]\n",
    "        pprod = js * psim\n",
    "        cprod = js * csim\n",
    "\n",
    "        pscore = 0\n",
    "        cscore = 0\n",
    "        try:\n",
    "            pscore = pprod[0, 0] - self.userEventScores[i, j]\n",
    "        except IndexError:\n",
    "            pass\n",
    "        try:\n",
    "            cscore = cprod[0, 0] - self.userEventScores[i, j]\n",
    "        except IndexError:\n",
    "            pass\n",
    "        return pscore, cscore\n",
    "\n",
    "    def userPop(self, userId):\n",
    "        \"\"\"\n",
    "        基于用户的朋友个数来推断用户的社交程度\n",
    "        主要的考量是如果用户的朋友非常多，可能会更倾向于参加各种社交活动\n",
    "        \"\"\"\n",
    "        if self.userIndex.has_key(userId):\n",
    "          i = self.userIndex[userId]\n",
    "          try:\n",
    "            return self.numFriends[0, i]\n",
    "          except IndexError:\n",
    "            return 0\n",
    "        else:\n",
    "            return 0.\n",
    "\n",
    "    def friendInfluence(self, userId):\n",
    "        \"\"\"\n",
    "        朋友对用户的影响\n",
    "        主要考虑用户所有的朋友中，有多少是非常喜欢参加各种社交活动/event的\n",
    "        用户的朋友圈如果都积极参与各种event，可能会对当前用户有一定的影响\n",
    "        \"\"\"\n",
    "        nusers = np.shape(self.userFriends)[1]\n",
    "        i = self.userIndex[userId]\n",
    "        return (self.userFriends[i, :].sum(axis=0) / nusers)[0,0]\n",
    "\n",
    "    def eventPop(self, eventId):\n",
    "        \"\"\"\n",
    "        本活动本身的热度\n",
    "        主要是通过参与的人数来界定的\n",
    "        \"\"\"\n",
    "        i = self.eventIndex[eventId]\n",
    "        return self.eventPopularity[i, 0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateRSData(RS, train=True, header=True):\n",
    "    \"\"\"\n",
    "    把前面user-based协同过滤 和 item-based协同过滤，以及各种热度和影响度作为特征组合在一起\n",
    "    生成新的训练数据，用于分类器分类使用\n",
    "    \"\"\"\n",
    "    fn = \"train.csv\" if train else \"test.csv\"\n",
    "    fin = open(fn, 'rb')\n",
    "    fout = open(\"RS_\" + fn, 'wb')\n",
    "    \n",
    "    #忽略第一行（列名字）\n",
    "    fin.readline().strip().split(\",\")\n",
    "    \n",
    "    # write output header\n",
    "    if header:\n",
    "        ocolnames = [\"invited\", \"userCF_reco\", \"evtCF_reco\",\"svdCF_reco\",\"user_reco\", \"evt_p_reco\",\n",
    "                     \"evt_c_reco\", \"user_pop\", \"frnd_infl\", \"evt_pop\"]\n",
    "    if train:\n",
    "        ocolnames.append(\"interested\")\n",
    "        ocolnames.append(\"not_interested\")\n",
    "    \n",
    "    fout.write(\",\".join(ocolnames) + \"\\n\")\n",
    "    \n",
    "    ln = 0\n",
    "    for line in fin:\n",
    "        ln += 1\n",
    "        if ln%500 == 0:\n",
    "            print \"%s:%d (userId, eventId)=(%s, %s)\" % (fn, ln, userId, eventId)\n",
    "            #break;\n",
    "            \n",
    "        cols = line.strip().split(\",\")\n",
    "        userId = cols[0]\n",
    "        eventId = cols[1]\n",
    "        invited = cols[2]\n",
    "        \n",
    "        userCF_reco = RS.userCFReco(userId, eventId)\n",
    "        itemCF_reco = RS.eventCFReco(userId, eventId)\n",
    "        svdCF_reco = RS.svdCFReco(userId, eventId)\n",
    "        \n",
    "        user_reco = RS.userReco(userId, eventId)\n",
    "        evt_p_reco, evt_c_reco = RS.eventReco(userId, eventId)\n",
    "        user_pop = RS.userPop(userId)\n",
    "        \n",
    "        frnd_infl = RS.friendInfluence(userId)\n",
    "        evt_pop = RS.eventPop(eventId)\n",
    "        ocols = [invited, userCF_reco, itemCF_reco, svdCF_reco,user_reco, evt_p_reco,\n",
    "                 evt_c_reco, user_pop, frnd_infl, evt_pop]\n",
    "      \n",
    "        if train:\n",
    "            ocols.append(cols[4]) # interested\n",
    "            ocols.append(cols[5]) # not_interested\n",
    "        \n",
    "        fout.write(\",\".join(map(lambda x: str(x), ocols)) + \"\\n\")\n",
    "        \n",
    "    fin.close()\n",
    "    fout.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVD Train...\n",
      "SVD trained\n",
      "生成训练数据...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramFiles\\Anaconda2\\lib\\site-packages\\scipy\\spatial\\distance.py:644: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = 1.0 - uv / np.sqrt(uu * vv)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train.csv:500 (userId, eventId)=(123290209, 1887085024)\n",
      "train.csv:1000 (userId, eventId)=(272886293, 199858305)\n",
      "train.csv:1500 (userId, eventId)=(395305791, 1582270949)\n",
      "train.csv:2000 (userId, eventId)=(527523423, 3272728211)\n",
      "train.csv:2500 (userId, eventId)=(651258472, 792632006)\n",
      "train.csv:3000 (userId, eventId)=(811791433, 524756826)\n",
      "train.csv:3500 (userId, eventId)=(985547042, 1269035551)\n",
      "train.csv:4000 (userId, eventId)=(1107615001, 173949238)\n",
      "train.csv:4500 (userId, eventId)=(1236336671, 3849306291)\n",
      "train.csv:5000 (userId, eventId)=(1414301782, 2652356640)\n",
      "train.csv:5500 (userId, eventId)=(1595465532, 955398943)\n",
      "train.csv:6000 (userId, eventId)=(1747091728, 2131379889)\n",
      "train.csv:6500 (userId, eventId)=(1914182220, 955398943)\n",
      "train.csv:7000 (userId, eventId)=(2071842684, 1076364848)\n",
      "train.csv:7500 (userId, eventId)=(2217853337, 3051438735)\n",
      "train.csv:8000 (userId, eventId)=(2338481531, 2525447278)\n",
      "train.csv:8500 (userId, eventId)=(2489551967, 520657921)\n",
      "train.csv:9000 (userId, eventId)=(2650493630, 87962584)\n",
      "train.csv:9500 (userId, eventId)=(2791418962, 4223848259)\n",
      "train.csv:10000 (userId, eventId)=(2903662804, 2791462807)\n",
      "train.csv:10500 (userId, eventId)=(3036141956, 3929507420)\n",
      "train.csv:11000 (userId, eventId)=(3176074542, 3459485614)\n",
      "train.csv:11500 (userId, eventId)=(3285425249, 2271782630)\n",
      "train.csv:12000 (userId, eventId)=(3410667855, 1063772489)\n",
      "train.csv:12500 (userId, eventId)=(3531604778, 2584839423)\n",
      "train.csv:13000 (userId, eventId)=(3686871863, 53495098)\n",
      "train.csv:13500 (userId, eventId)=(3833637800, 2415873572)\n",
      "train.csv:14000 (userId, eventId)=(3944021305, 2096772901)\n",
      "train.csv:14500 (userId, eventId)=(4075466480, 3567240505)\n",
      "train.csv:15000 (userId, eventId)=(4197193550, 1628057176)\n",
      "生成预测数据...\n",
      "\n",
      "test.csv:500 (userId, eventId)=(182290053, 2529072432)\n",
      "test.csv:1000 (userId, eventId)=(433510318, 4244463632)\n",
      "test.csv:1500 (userId, eventId)=(632808865, 2845303452)\n",
      "test.csv:2000 (userId, eventId)=(813611885, 2036538169)\n",
      "test.csv:2500 (userId, eventId)=(1010701404, 303459881)\n",
      "test.csv:3000 (userId, eventId)=(1210932037, 2529072432)\n",
      "test.csv:3500 (userId, eventId)=(1452921099, 2705317682)\n",
      "test.csv:4000 (userId, eventId)=(1623287180, 1626678328)\n",
      "test.csv:4500 (userId, eventId)=(1855201342, 2603032829)\n",
      "test.csv:5000 (userId, eventId)=(2083900381, 2529072432)\n",
      "test.csv:5500 (userId, eventId)=(2318415276, 2509151803)\n",
      "test.csv:6000 (userId, eventId)=(2528161539, 4025975316)\n",
      "test.csv:6500 (userId, eventId)=(2749110768, 4244406355)\n",
      "test.csv:7000 (userId, eventId)=(2927772127, 1532377761)\n",
      "test.csv:7500 (userId, eventId)=(3199685636, 1776393554)\n",
      "test.csv:8000 (userId, eventId)=(3393388475, 680270887)\n",
      "test.csv:8500 (userId, eventId)=(3601169721, 154434302)\n",
      "test.csv:9000 (userId, eventId)=(3828963415, 3067222491)\n",
      "test.csv:9500 (userId, eventId)=(4018723397, 2522610844)\n",
      "test.csv:10000 (userId, eventId)=(4180064266, 2658555390)\n"
     ]
    }
   ],
   "source": [
    "RS = RecommonderSystem()\n",
    "print \"生成训练数据...\\n\"\n",
    "generateRSData(RS,train=True,  header=True)\n",
    "\n",
    "print \"生成预测数据...\\n\"\n",
    "generateRSData(RS, train=False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间、地点等特征都没有处理了，可以考虑用户看到event的时间与event开始时间的差、用户地点和event地点的差异。。。"
   ]
  }
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